An artificial intelligence approach to fault isolation based on sensor data in Tennessee Eastman process

Majid Ghaniee Zarch; Mohsen Soltani

doi:10.1109/IECON43393.2020.9255330

An artificial intelligence approach to fault isolation based on sensor data in Tennessee Eastman process

Majid Ghaniee Zarch, Mohsen Soltani

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

1 Citation (Scopus)

Abstract

An effective fault diagnosis scheme can improve system's safety and reliability. Artificial Intelligence (AI) provides a good framework to deal with this issue. Deep learning is a successful implementation of AI that its superior isolation performance find its way in fault diagnosis area. In this study, based on feature extraction abilities of Convolutional Neural Network (CNN), a deep network have been developed in order to isolate different kinds of faults in Tennessee Eastman process. This network has an end-to-end structure with 13 layers that takes raw sensor's data and has isolation performance of more than 98 percent. A comparison between our proposed method and a linear classifier that uses Principal Component Analysis(PCA) for feature extraction and a Neural Network (NN) with 2 hidden layers as nonlinear classifier have been conducted to show the performance of the proposed fault isolation scheme.

Original language	English
Title of host publication	IECON 2020 : The 46th Annual Conference of the IEEE Industrial Electronics Society
Number of pages	6
Publisher	IEEE
Publication date	18 Oct 2020
Pages	417-422
Article number	9255330
ISBN (Print)	978-1-7281-5415-2
ISBN (Electronic)	9781728154145
DOIs	https://doi.org/10.1109/IECON43393.2020.9255330
Publication status	Published - 18 Oct 2020
Event	46th Annual Conference of the IEEE Industrial Electronics Society, IECON 2020 - Virtual, Singapore, Singapore Duration: 18 Oct 2020 → 21 Oct 2020 http://www.conferences.academicjournals.org/cat/physical-sciences/46th-annual-conference-of-the-ieee-industrial-electronics-society

Conference

Conference	46th Annual Conference of the IEEE Industrial Electronics Society, IECON 2020
Country/Territory	Singapore
City	Virtual, Singapore
Period	18/10/2020 → 21/10/2020
Sponsor	IEEE Industrial Electronics Society (IES), SPECS - Smart Grid + Power Electronics Consortium Singapore, The Institute of Electrical and Electronics Engineers (IEEE)
Internet address	http://www.conferences.academicjournals.org/cat/physical-sciences/46th-annual-conference-of-the-ieee-industrial-electronics-society

Series	Proceedings of the Annual Conference of the IEEE Industrial Electronics Society
ISSN	1553-572X

Keywords

Artificial intelligence
Convolutional neural network
Deep learning
Fault detection and isolation
Sensor data
Tennessee Eastman process

Access to Document

10.1109/IECON43393.2020.9255330

AUB Link

Search for the material in Aalborg University Library's search engine

Cite this

@inproceedings{4012e903198b4936adea438ba1565166,

title = "An artificial intelligence approach to fault isolation based on sensor data in Tennessee Eastman process",

abstract = "An effective fault diagnosis scheme can improve system's safety and reliability. Artificial Intelligence (AI) provides a good framework to deal with this issue. Deep learning is a successful implementation of AI that its superior isolation performance find its way in fault diagnosis area. In this study, based on feature extraction abilities of Convolutional Neural Network (CNN), a deep network have been developed in order to isolate different kinds of faults in Tennessee Eastman process. This network has an end-to-end structure with 13 layers that takes raw sensor's data and has isolation performance of more than 98 percent. A comparison between our proposed method and a linear classifier that uses Principal Component Analysis(PCA) for feature extraction and a Neural Network (NN) with 2 hidden layers as nonlinear classifier have been conducted to show the performance of the proposed fault isolation scheme.",

keywords = "Artificial intelligence, Convolutional neural network, Deep learning, Fault detection and isolation, Sensor data, Tennessee Eastman process",

author = "Zarch, {Majid Ghaniee} and Mohsen Soltani",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 46th Annual Conference of the IEEE Industrial Electronics Society, IECON 2020 ; Conference date: 18-10-2020 Through 21-10-2020",

year = "2020",

month = oct,

day = "18",

doi = "10.1109/IECON43393.2020.9255330",

language = "English",

isbn = "978-1-7281-5415-2",

series = "Proceedings of the Annual Conference of the IEEE Industrial Electronics Society",

publisher = "IEEE",

pages = "417--422",

booktitle = "IECON 2020",

address = "United States",

url = "http://www.conferences.academicjournals.org/cat/physical-sciences/46th-annual-conference-of-the-ieee-industrial-electronics-society",

}

Zarch, MG & Soltani, M 2020, An artificial intelligence approach to fault isolation based on sensor data in Tennessee Eastman process. in IECON 2020 : The 46th Annual Conference of the IEEE Industrial Electronics Society., 9255330, IEEE, Proceedings of the Annual Conference of the IEEE Industrial Electronics Society, pp. 417-422, 46th Annual Conference of the IEEE Industrial Electronics Society, IECON 2020, Virtual, Singapore, Singapore, 18/10/2020. https://doi.org/10.1109/IECON43393.2020.9255330

An artificial intelligence approach to fault isolation based on sensor data in Tennessee Eastman process. / Zarch, Majid Ghaniee; Soltani, Mohsen.
IECON 2020 : The 46th Annual Conference of the IEEE Industrial Electronics Society. IEEE, 2020. p. 417-422 9255330 (Proceedings of the Annual Conference of the IEEE Industrial Electronics Society).

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

TY - GEN

T1 - An artificial intelligence approach to fault isolation based on sensor data in Tennessee Eastman process

AU - Zarch, Majid Ghaniee

AU - Soltani, Mohsen

PY - 2020/10/18

Y1 - 2020/10/18

N2 - An effective fault diagnosis scheme can improve system's safety and reliability. Artificial Intelligence (AI) provides a good framework to deal with this issue. Deep learning is a successful implementation of AI that its superior isolation performance find its way in fault diagnosis area. In this study, based on feature extraction abilities of Convolutional Neural Network (CNN), a deep network have been developed in order to isolate different kinds of faults in Tennessee Eastman process. This network has an end-to-end structure with 13 layers that takes raw sensor's data and has isolation performance of more than 98 percent. A comparison between our proposed method and a linear classifier that uses Principal Component Analysis(PCA) for feature extraction and a Neural Network (NN) with 2 hidden layers as nonlinear classifier have been conducted to show the performance of the proposed fault isolation scheme.

AB - An effective fault diagnosis scheme can improve system's safety and reliability. Artificial Intelligence (AI) provides a good framework to deal with this issue. Deep learning is a successful implementation of AI that its superior isolation performance find its way in fault diagnosis area. In this study, based on feature extraction abilities of Convolutional Neural Network (CNN), a deep network have been developed in order to isolate different kinds of faults in Tennessee Eastman process. This network has an end-to-end structure with 13 layers that takes raw sensor's data and has isolation performance of more than 98 percent. A comparison between our proposed method and a linear classifier that uses Principal Component Analysis(PCA) for feature extraction and a Neural Network (NN) with 2 hidden layers as nonlinear classifier have been conducted to show the performance of the proposed fault isolation scheme.

KW - Artificial intelligence

KW - Convolutional neural network

KW - Deep learning

KW - Fault detection and isolation

KW - Sensor data

KW - Tennessee Eastman process

UR - http://www.scopus.com/inward/record.url?scp=85097770353&partnerID=8YFLogxK

U2 - 10.1109/IECON43393.2020.9255330

DO - 10.1109/IECON43393.2020.9255330

M3 - Article in proceeding

AN - SCOPUS:85097770353

SN - 978-1-7281-5415-2

T3 - Proceedings of the Annual Conference of the IEEE Industrial Electronics Society

SP - 417

EP - 422

BT - IECON 2020

PB - IEEE

T2 - 46th Annual Conference of the IEEE Industrial Electronics Society, IECON 2020

Y2 - 18 October 2020 through 21 October 2020

ER -

An artificial intelligence approach to fault isolation based on sensor data in Tennessee Eastman process

Abstract

Conference

Keywords

Access to Document

AUB Link

Other files and links

Fingerprint

Cite this